{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from collections import defaultdict\n",
    "from pprint import pprint\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from torch.utils.data import DataLoader\n",
    "from pandas import DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from nupic.research.frameworks.pytorch.model_utils import count_nonzero_params\n",
    "from nupic.research.frameworks.pytorch.restore_utils import load_state_from_checkpoint"
   ]
  },
  {
   "source": [
    "## Overview\n",
    "This notebook calculate \n",
    "* How often each unit is on, averages accross the whole datset\n",
    "* The level of sparsity seen for each output to a layer\n",
    "* The entropy of each unit $$-\\sum p_i \\log p_i $$ where $p_i$ is the fraction of times the unit is on throughout the whole dataset."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Load the experiment\n",
    "\n",
    "This will load the desired experiment config and related Dataloader."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 16\n",
    "PROJECT_DIR = os.path.expanduser(\"~/nta/nupic.research/projects/gsc\")\n",
    "EXPERIMENT_NAME = \"default_sparse_cnn\"\n",
    "CHECKPOINT_FILE = \"/home/ec2-user/nta/results/experiments/gsc/default_sparse_cnn/RemoteProcessTrainable_0_2021-01-07_15-54-473wnu2slv/checkpoint_30/checkpoint\"\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "\n",
    "# Load the experiment\n",
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.insert(0, PROJECT_DIR)\n",
    "\n",
    "from experiments import CONFIGS\n",
    "\n",
    "config = CONFIGS[EXPERIMENT_NAME]\n",
    "exp_cls = config[\"experiment_class\"]\n",
    "exp = exp_cls()\n",
    "\n",
    "# Load dataloader with specified BATCH_SIZE.\n",
    "dataset = exp_cls.load_dataset(config, train=True)\n",
    "train_loader = DataLoader(\n",
    "    dataset=dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    num_workers=0,\n",
    "    sampler=None,\n",
    "    pin_memory=USE_CUDA,\n",
    "    drop_last=config.get(\"train_loader_drop_last\", False),\n",
    ")"
   ]
  },
  {
   "source": [
    "## The the model from a checkpoint."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to checkpoint.\n",
    "assert CHECKPOINT_FILE is not None, \"You should specify a path to a checkpoint.\"\n",
    "\n",
    "# Load model\n",
    "config = CONFIGS[EXPERIMENT_NAME]\n",
    "exp_cls = config[\"experiment_class\"]\n",
    "exp = exp_cls()\n",
    "\n",
    "model = exp_cls.create_model(config, \"cuda\")\n",
    "load_state_from_checkpoint(model=model, checkpoint_path=CHECKPOINT_FILE, device=\"cuda\");\n"
   ]
  },
  {
   "source": [
    "## Helper class and method."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "class SaveForwardOutput:\n",
    "    \"\"\"\n",
    "    Forward hook for atorch.nn.Module. This will store the output from a module during the forward pass.\n",
    "\n",
    "    :param name: name of the module (e.g. \"classifier\")\n",
    "    :param verbose: whether to print something every forward pass\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, name=None, verbose=False):\n",
    "        self.name = name\n",
    "        self.verbose = verbose\n",
    "        self.output = None\n",
    "    \n",
    "    def __call__(self, module, x, y):\n",
    "        \"\"\"\n",
    "        Forward hook on torch.nn.Module\n",
    "\n",
    "        :param module: module\n",
    "        :param x: tuple of inputs\n",
    "        :param y: ouput of module\n",
    "        \"\"\"\n",
    "        if self.verbose:\n",
    "            print(f\"Forward hook of {self.name} ({module.__class__.__name__})\")\n",
    "\n",
    "        # Save the output of the layer.\n",
    "        self.output = y\n",
    "\n",
    "def calculate_on_counts(rep):\n",
    "    \"\"\"\n",
    "    Calculates the number of times a unit is on for the given batch.\n",
    "\n",
    "    :param rep: tensor of an intermediate representation\n",
    "    :return: a 1D tensor of with len equal to the total number of units\n",
    "    \"\"\"\n",
    "    rep = rep.flatten(start_dim=1)\n",
    "    on_mask = rep != 0\n",
    "    on_counts = on_mask.sum(axis=0)\n",
    "    return on_counts\n",
    "\n",
    "def calculate_sparsities(rep):\n",
    "    \"\"\"\n",
    "    Calculates the fraction of units off for each sample in the batch.\n",
    "\n",
    "    :param rep: tensor of an intermediate representation\n",
    "    :return: tensor of sparsities, one for each sample in the batch\n",
    "    \"\"\"\n",
    "    rep = rep.flatten(start_dim=1)\n",
    "    off_mask = rep == 0\n",
    "    sparsities = off_mask.sum(axis=1).float()\n",
    "    sparsities = sparsities / rep.shape[1]\n",
    "    return sparsities\n"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 7,
   "outputs": []
  },
  {
   "source": [
    "## Add forward hooks to all layers of the model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "forward_hooks = {}\n",
    "tracked_modules = []\n",
    "\n",
    "for n, m in model.named_modules():\n",
    "\n",
    "    # Skip over modules that have sub-modules.\n",
    "    # These sub-modules will be found recursively by 'named_modules'. \n",
    "    if len(list(m.children())) > 0:\n",
    "        continue\n",
    "\n",
    "    # TODO: We can filter which modules get tracked if needed.\n",
    "    forward_hook = SaveForwardOutput(name=n)\n",
    "    m.register_forward_hook(forward_hook)\n",
    "    forward_hooks[n] = forward_hook\n",
    "    tracked_modules.append(m)"
   ]
  },
  {
   "source": [
    "## A Brief Overview of the Model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model density (on_params / total_params): 194,388 / 1,717,140 = 0.11\n"
     ]
    }
   ],
   "source": [
    "total_params, on_params = count_nonzero_params(model)\n",
    "print(f\"Model density (on_params / total_params): {on_params:,} / {total_params:,} = {on_params/total_params:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "GSCSparseCNN(\n  (cnn1): SparseWeights2d(\n    sparsity=0.5\n    (module): Conv2d(1, 64, kernel_size=(5, 5), stride=(1, 1))\n  )\n  (cnn1_batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n  (cnn1_kwinner): KWinners2d(channels=64, local=False, break_ties=False, n=0, percent_on=0.095, boost_strength=0.06358673423528671, boost_strength_factor=0.9, k_inference_factor=1.0, duty_cycle_period=1000)\n  (cnn1_maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n  (cnn2): SparseWeights2d(\n    sparsity=0.8\n    (module): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1))\n  )\n  (cnn2_batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n  (cnn2_kwinner): KWinners2d(channels=64, local=False, break_ties=False, n=0, percent_on=0.125, boost_strength=0.06358673423528671, boost_strength_factor=0.9, k_inference_factor=1.0, duty_cycle_period=1000)\n  (cnn2_maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n  (flatten): Flatten()\n  (linear): SparseWeights(\n    sparsity=0.9\n    (module): Linear(in_features=1600, out_features=1000, bias=True)\n  )\n  (linear_bn): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)\n  (linear_kwinner): KWinners(n=1000, percent_on=0.1, boost_strength=0.06358673423528671, boost_strength_factor=0.9, k_inference_factor=1.0, duty_cycle_period=1000, break_ties=False)\n  (output): Linear(in_features=1000, out_features=12, bias=True)\n  (softmax): LogSoftmax()\n)\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "source": [
    "## Compute statistics of the output representations\n",
    "* How often each unit is on\n",
    "* The level of sparsity for each output from a layer"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This will be a tensor of length equal to the total number of output units to the given layer.\n",
    "# Each value will represent the frequency (\"freq\") of how often that unit is on throughout the whol dataset.\n",
    "layer_on_freqs = defaultdict(int)\n",
    "\n",
    "# This will be a list of sparsities measure for each output passed to the layer.\n",
    "layer_sparsities = defaultdict(list)\n",
    "\n",
    "# Loop through the whole dataset. See \"Load the experiment\" section\n",
    "# above to use the validation dataloader if desired.\n",
    "total_images = 0\n",
    "model.eval()\n",
    "for x, y in train_loader:\n",
    "    x, y = x.to(\"cuda\"), y.to(\"cuda\")\n",
    "    out = model(x)\n",
    "\n",
    "    for name, hook in forward_hooks.items():\n",
    "\n",
    "        # Retrieve ouput saved in the forward hook.\n",
    "        representation = hook.output\n",
    "\n",
    "        # Tally the on-counts for each unit.\n",
    "        on_counts = calculate_on_counts(representation)\n",
    "        layer_on_freqs[name] += on_counts\n",
    "\n",
    "        # Compute the level of sparsity for each output.\n",
    "        sparsities = calculate_sparsities(representation)\n",
    "        layer_sparsities[name].extend(sparsities.tolist())\n",
    "\n",
    "    total_images += len(x)\n",
    "\n",
    "# Divide through by number of samples to find on-frequency for eash unit.\n",
    "for layer, on_counts in layer_on_freqs.items():\n",
    "    layer_on_freqs[layer] = on_counts.float() / total_images\n"
   ]
  },
  {
   "source": [
    "## Print summary of statitics\n",
    "* How often each unit is on\n",
    "* The level of sparsity for each output to a layer\n",
    "* The entropy of each unit (excluding dead units; otherwise we take the lof of zero)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Layer cnn1.module (Conv2d)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 0.0\n    Fractions of dead neurons in layer = 0.000 (0 out of 50176)\n\nLayer cnn1_batchnorm (BatchNorm2d)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 4.280653698174319e-08\n    Fractions of dead neurons in layer = 0.000 (0 out of 50176)\n\nLayer cnn1_kwinner (KWinners2d)\n    Average sparsity of output = 0.903 ± 0.004\n    Entropy of layer = 0.19471943378448486\n    Fractions of dead neurons in layer = 0.000 (0 out of 50176)\n\nLayer cnn1_maxpool (MaxPool2d)\n    Average sparsity of output = 0.851 ± 0.016\n    Entropy of layer = 0.24615955352783203\n    Fractions of dead neurons in layer = 0.000 (0 out of 12544)\n\nLayer cnn2.module (Conv2d)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 0.0\n    Fractions of dead neurons in layer = 0.000 (0 out of 6400)\n\nLayer cnn2_batchnorm (BatchNorm2d)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 2.288203759803764e-08\n    Fractions of dead neurons in layer = 0.000 (0 out of 6400)\n\nLayer cnn2_kwinner (KWinners2d)\n    Average sparsity of output = 0.875 ± 0.000\n    Entropy of layer = 0.2465870976448059\n    Fractions of dead neurons in layer = 0.000 (0 out of 6400)\n\nLayer cnn2_maxpool (MaxPool2d)\n    Average sparsity of output = 0.749 ± 0.042\n    Entropy of layer = 0.3296735882759094\n    Fractions of dead neurons in layer = 0.000 (0 out of 1600)\n\nLayer flatten (Flatten)\n    Average sparsity of output = 0.749 ± 0.042\n    Entropy of layer = 0.3296735882759094\n    Fractions of dead neurons in layer = 0.000 (0 out of 1600)\n\nLayer linear.module (Linear)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 0.0\n    Fractions of dead neurons in layer = 0.000 (0 out of 1000)\n\nLayer linear_bn (BatchNorm1d)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 9.763002850604607e-08\n    Fractions of dead neurons in layer = 0.000 (0 out of 1000)\n\nLayer linear_kwinner (KWinners)\n    Average sparsity of output = 0.900 ± 0.000\n    Entropy of layer = 0.22677914798259735\n    Fractions of dead neurons in layer = 0.000 (0 out of 1000)\n\nLayer output (Linear)\n    Average sparsity of output = 0.000 ± 0.000\n    Entropy of layer = 0.0\n    Fractions of dead neurons in layer = 0.000 (0 out of 12)\n\nLayer softmax (LogSoftmax)\n    Average sparsity of output = 0.000 ± 0.001\n    Entropy of layer = 2.034376302617602e-05\n    Fractions of dead neurons in layer = 0.000 (0 out of 12)\n\n"
     ]
    }
   ],
   "source": [
    "df = DataFrame(columns=[\"module type\", \"mu output sparsity\", \"sd output sparsity\", \"entropy\", \"dead neuron count\", \"total neurons\", \"fraction_dead\"])\n",
    "\n",
    "\n",
    "def log2_of_zeros(arr):\n",
    "    \"\"\"\n",
    "    Intentionally leave zero's as they are when taking the log.\n",
    "    \"\"\"\n",
    "    arr = arr.copy()\n",
    "    arr[arr != 0] = np.log(arr[arr != 0])\n",
    "    return arr\n",
    "\n",
    "\n",
    "for (layer, sparsities), freqs, module in zip(layer_sparsities.items(), layer_on_freqs.values(), tracked_modules):\n",
    "\n",
    "    mu_sparsity = np.mean(sparsities)\n",
    "    sd_sparsity = np.std(sparsities)\n",
    "    module_type = module.__class__.__name__\n",
    "    print(f\"Layer {layer} ({module_type})\")\n",
    "    print(f\"    Average sparsity of output = {mu_sparsity:.3f} ± {sd_sparsity:.3f}\")\n",
    "\n",
    "    freqs = freqs.cpu().numpy()\n",
    "    entropy = np.sum(-freqs * log2_of_zeros(freqs) / len(freqs))\n",
    "    print(f\"    Entropy of layer = {entropy}\")\n",
    "\n",
    "    dead_count = (freqs == 0).sum()\n",
    "    total_neurons = len(freqs)\n",
    "    fraction_dead = dead_count / total_neurons\n",
    "    print(f\"    Fractions of dead neurons in layer = {fraction_dead:.3f} ({dead_count} out of {total_neurons})\")\n",
    "    print()\n",
    "\n",
    "    df.at[layer] = (module_type, mu_sparsity, sd_sparsity, entropy, dead_count, total_neurons, fraction_dead)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                module type mu output sparsity sd output sparsity  \\\n",
       "cnn1.module          Conv2d                  0                  0   \n",
       "cnn1_batchnorm  BatchNorm2d        4.28138e-08        9.22735e-07   \n",
       "cnn1_kwinner     KWinners2d           0.903458         0.00412662   \n",
       "cnn1_maxpool      MaxPool2d           0.851052          0.0163166   \n",
       "cnn2.module          Conv2d                  0                  0   \n",
       "cnn2_batchnorm  BatchNorm2d        2.28859e-08        1.89087e-06   \n",
       "cnn2_kwinner     KWinners2d           0.874997        4.19071e-05   \n",
       "cnn2_maxpool      MaxPool2d           0.749466          0.0423709   \n",
       "flatten             Flatten           0.749466          0.0423709   \n",
       "linear.module        Linear                  0                  0   \n",
       "linear_bn       BatchNorm1d        9.76467e-08        9.88115e-06   \n",
       "linear_kwinner     KWinners                0.9                  0   \n",
       "output               Linear                  0                  0   \n",
       "softmax          LogSoftmax        2.03431e-05         0.00130186   \n",
       "\n",
       "                    entropy dead neuron count total neurons fraction_dead  \n",
       "cnn1.module               0                 0         50176             0  \n",
       "cnn1_batchnorm  4.28065e-08                 0         50176             0  \n",
       "cnn1_kwinner       0.194719                 0         50176             0  \n",
       "cnn1_maxpool        0.24616                 0         12544             0  \n",
       "cnn2.module               0                 0          6400             0  \n",
       "cnn2_batchnorm   2.2882e-08                 0          6400             0  \n",
       "cnn2_kwinner       0.246587                 0          6400             0  \n",
       "cnn2_maxpool       0.329674                 0          1600             0  \n",
       "flatten            0.329674                 0          1600             0  \n",
       "linear.module             0                 0          1000             0  \n",
       "linear_bn         9.763e-08                 0          1000             0  \n",
       "linear_kwinner     0.226779                 0          1000             0  \n",
       "output                    0                 0            12             0  \n",
       "softmax         2.03438e-05                 0            12             0  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>module type</th>\n      <th>mu output sparsity</th>\n      <th>sd output sparsity</th>\n      <th>entropy</th>\n      <th>dead neuron count</th>\n      <th>total neurons</th>\n      <th>fraction_dead</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>cnn1.module</th>\n      <td>Conv2d</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50176</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn1_batchnorm</th>\n      <td>BatchNorm2d</td>\n      <td>4.28138e-08</td>\n      <td>9.22735e-07</td>\n      <td>4.28065e-08</td>\n      <td>0</td>\n      <td>50176</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn1_kwinner</th>\n      <td>KWinners2d</td>\n      <td>0.903458</td>\n      <td>0.00412662</td>\n      <td>0.194719</td>\n      <td>0</td>\n      <td>50176</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn1_maxpool</th>\n      <td>MaxPool2d</td>\n      <td>0.851052</td>\n      <td>0.0163166</td>\n      <td>0.24616</td>\n      <td>0</td>\n      <td>12544</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn2.module</th>\n      <td>Conv2d</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6400</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn2_batchnorm</th>\n      <td>BatchNorm2d</td>\n      <td>2.28859e-08</td>\n      <td>1.89087e-06</td>\n      <td>2.2882e-08</td>\n      <td>0</td>\n      <td>6400</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn2_kwinner</th>\n      <td>KWinners2d</td>\n      <td>0.874997</td>\n      <td>4.19071e-05</td>\n      <td>0.246587</td>\n      <td>0</td>\n      <td>6400</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cnn2_maxpool</th>\n      <td>MaxPool2d</td>\n      <td>0.749466</td>\n      <td>0.0423709</td>\n      <td>0.329674</td>\n      <td>0</td>\n      <td>1600</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>flatten</th>\n      <td>Flatten</td>\n      <td>0.749466</td>\n      <td>0.0423709</td>\n      <td>0.329674</td>\n      <td>0</td>\n      <td>1600</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>linear.module</th>\n      <td>Linear</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>linear_bn</th>\n      <td>BatchNorm1d</td>\n      <td>9.76467e-08</td>\n      <td>9.88115e-06</td>\n      <td>9.763e-08</td>\n      <td>0</td>\n      <td>1000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>linear_kwinner</th>\n      <td>KWinners</td>\n      <td>0.9</td>\n      <td>0</td>\n      <td>0.226779</td>\n      <td>0</td>\n      <td>1000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>output</th>\n      <td>Linear</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>softmax</th>\n      <td>LogSoftmax</td>\n      <td>2.03431e-05</td>\n      <td>0.00130186</td>\n      <td>2.03438e-05</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "source": [
    "## Visualize the distribution of how often each unit is on or off\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Names of layers observed via hooks: (helpful for specifying 'layer' in the cell below.\n\n['cnn1.module',\n 'cnn1_batchnorm',\n 'cnn1_kwinner',\n 'cnn1_maxpool',\n 'cnn2.module',\n 'cnn2_batchnorm',\n 'cnn2_kwinner',\n 'cnn2_maxpool',\n 'flatten',\n 'linear.module',\n 'linear_bn',\n 'linear_kwinner',\n 'output',\n 'softmax']\n"
     ]
    }
   ],
   "source": [
    "print(\"Names of layers observed via hooks: (helpful for specifying 'layer' in the cell below.\\n\")\n",
    "pprint(list(layer_on_freqs.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Choose one layer to visualize the frequency of it's output units being on or off.\n",
    "layer = \"linear_kwinner\"\n",
    "\n",
    "on_freqs = layer_on_freqs[layer].cpu().numpy()\n",
    "\n",
    "fig = sns.histplot(data=on_freqs, kde=True, stat=\"probability\")\n",
    "fig.set_xlabel(\"on-frequency\")\n",
    "fig.set_title(f\"How often are units on for layer '{layer}'\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}